Employing CNNs in semantic segmentation tasks has been proven to be extremely helpful in various applications, including object tracking [1]}, [2]}, [3]}, self-driving cars [4]}, [5]}, and medical image analysis [6]}, [7]}, [8]}, [9]}. This success, however, is conditioned on the availability of huge manually annotated datasets to supervise the training of state-of-the-art (SOTA) networks structures [10]}, [11]}. This condition is not always realized in practice, especially in fields such as medical image segmentation, where annotating data requires the input of trained experts and privacy regulations make sharing data for crowd-sourcing extremely restricted, and at times impossible. A characteristic of data in the area of medical image segmentation is the existence of domain shift between different imaging modalities which results from using imaging devices that are based on totally different electromagnetic principles, e.g., CT vs MRI. When domain gap exists between the distributions of the training (source) and the testing (target) data, the performance of CNNs can degrade significantly. This makes continual data annotation necessary for mode retraining.
